Mobile emissions represent a significant fraction of the total anthropogenic emissions burden in megacities and have a deleterious effect on air quality at local and regional scales. Due to the significant sources of uncertainties involved during the estimation of mobile emissions, an adequate treatment of emission uncertainties is critical during the design of air quality control strategies using AQMs. This thesis focuses on quantifying the effects of parametric uncertainties of input emission fields on model uncertainties of ozone predictions. We obtained direct measurements of mobile emission sources in the Mexico City Metropolitan Area (MCMA) using a novel measurement technique and quantified the magnitude and variability of key pollutant species. This analysis allowed a direct evaluation of the emissions inventory used in AQMs for the MCMA. Measured selected VOCs and NOy showed a strong dependence on traffic mode and indicated a larger than expected burden of emitted NOx and aldehydes. Our measurements of benzene, toluene, formaldehyde, and acetaldehyde in the MCMA indicate that the emissions of these toxic pollutants are similar or higher than for some US cities. We derived approximate historical trends of the VOC/NOx emission ratio and quantified the impact of changes of mobile emission sources on the photochemical levels using the Brute Force Method and Direct Decoupled Method sensitivity techniques with the CAMx model. The model reasonably reproduces concentrations of ozone and VOCs and accurately those of CO and NOx but over predicts OH by about 25% and severely under-predicts HO2 by a factor of 2 to 3 suggesting that the radical formation pathways in current state of the art AQMs should be revised.(cont.) The model successfully reproduces the corresponding relative changes in historical observations of ozone peak and diurnal average concentrations and suggests a current moderate VOC-sensitive regime. The analysis of the model's sensitivity coefficients to individual perturbations of VOC group species as described by the SAPRC99 chemical mechanism showed that the model is particularly sensitive to aromatics, higher alkenes, and formaldehyde emissions. We found, however, that NOx, olefins and aromatic species can potentially contribute significantly to uncertainties in ozone predictions.